Fingerprint sensing and matching is a reliable and widely used technique for personal identification or verification. In particular, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference or enrolled fingerprints already in a database to determine proper identification of a person, such as for verification purposes.
A significant advance in the area of fingerprint sensing is disclosed in U.S. Pat. No. 5,940,526 to Setlak et al. and assigned to the assignee of the present invention. The patent discloses an integrated circuit fingerprint sensor including an array of RF sensing electrodes to provide an accurate image of the fingerprint friction ridges and valleys. More particularly, the RF sensing permits imaging of live tissue just below the surface of the skin to reduce spoofing, for example. The entire contents of the Setlak et al. patent are incorporated herein by reference.
Fingerprint matching approaches can be generally classified into two classes: minutia-based and pattern-based. Minutia-based approaches rely on minutia features, such as ridge ends and bifurcations. On the other hand, pattern-based approaches rely on fingerprint patterns such as image pixel values, ridge orientation and ridge frequency. Pattern-based approaches may be superior to minutia-based approaches when having images of poor quality or when using a small sensor.
A common pattern-based approach relies on image sub-regions, referred to as spots. In this approach, a number of spots are extracted from a fingerprint image used for enrollment. Each spot is then correlated with a fingerprint image used for authentication or verification. This process generates a best correlation score along with the transformation that the given spot undergoes to generate the best score.
The spot transformation includes a two-dimensional translation and possibly a rotation. The final matching score is a function of both the individual correlation scores, for all spots, and the geometric consistency information among spot transformations. Examples of this approach are disclosed in A. M. Bazen, G. T. B. Verwaaijen, S. H. Gerez, L. P. J. Veelenturf and B. J. van der Zwaag, A correlation-based fingerprint verification system, Proc. Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, 2000; Z. M. Kovacs-Vajna, A fingerprint verification system based on triangular matching and dynamic time warping, IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 22, no. 11, November 2000; U.S. Pat. No. 6,241,288 to Bergenek et al.; U.S. Pat. No. 6,134,340 to Hsu et al.; and U.S. Pat. No. 5,067,162 to Driscoll, Jr. et al. The entire discloses of each of these references being incorporated herein by reference.
In another potential scenario, the alignment between the enrollment and verify fingerprints is determined using a fast matcher (e.g., one that uses minutiae or ridge orientation). The alignment is further verified using spots correlated with the match fingerprint image within a small acceptable transformation subspace centered at the transformation provided by the initial matcher. In this case, the overall spot-based score is a function of all individual correlation scores. The overall spot-based score is further combined with the score generated by the first matcher to generate a final matching score. The latter approach is expected to have a superior performance to the former one since it uses more information. In addition, it is also expected to be more efficient since it avoids the exhaustive correlation of each spot against the match fingerprint image.
Despite continued developments in finger biometric matching using spots, such may still not provide accurate matching and while being readily implemented.